# iterate over Result
dset = [
{'col':['Power_PowerAct'], '_ylim':(0,5000), 'color':'red'},
{'col':['Various_Values_SpeedAct'],'_ylim': [0, 2500], 'color':'blue'},
{'col':['Hyd_PressCrankCase'],'_ylim': [-100, 100], 'color':'orange'},
{'col':['Hyd_PressOilDif'],'_ylim': [0, 2], 'color':'black'},
{'col':['Hyd_TempOil'],'_ylim': [60, 110], 'color':'#2171b5'}
]
def crawl_result(rda, srange = None, p_figsize=(16,10)):
if srange == None:
srange = [s['index'] for i,s in rda.iterrows()] # too much memory needed !!
#srange = [s for s in range(2)] # limit to 2 in the interim
else:
if isinstance(srange,int):
srange = [srange]
elif not isinstance(srange,list):
raise ValueError(f"crawl_result: invalid parameter! srange={srange}")
print(srange)
for ii,startversuch in rda.iterrows():
if startversuch['index'] in srange:
data = fsm.get_cycle_data(startversuch, max_length=None, min_length=None, cycletime=1, silent=True, p_data=fsm._data_spec + ['Hyd_PressCrankCase','Hyd_PressOilDif','Hyd_TempOil'])
print(data.shape)
pl, _ = fsm.detect_edge_left(data, 'Power_PowerAct', startversuch)
pr, _ = fsm.detect_edge_right(data, 'Power_PowerAct', startversuch)
sl, _ = fsm.detect_edge_left(data, 'Various_Values_SpeedAct', startversuch)
sr, _ = fsm.detect_edge_right(data, 'Various_Values_SpeedAct', startversuch)
# code ?
ml = (data.iloc[-1]['time'] - data.iloc[0]['time']) // 1000
sv_lines = [v for v in startversuch[fsm.filters['vertical_lines_times']]]
nsv_lines = [v for v in sv_lines if ((v==v) and (v <= ml)) ]
svdf = pd.DataFrame(sv_lines, index=fsm.filters['vertical_lines_times'], columns=['FSM']).fillna(0)
svdf['RUN2'] = svdf['FSM']
#if svdf.at['hochlauf','FSM'] > 0.0:
# svdf.at['hochlauf','RUN2'] = sl.loc.timestamp() - start.timestamp() - np.cumsum(svdf['RUN2'])['starter']
# svdf.at['idle','RUN2'] = svdf.at['idle','FSM'] - (svdf.at['hochlauf','RUN2'] - svdf.at['hochlauf','FSM'])
if svdf.at['loadramp','FSM'] > 0.0:
svdf.at['loadramp','RUN2'] = pl.loc.timestamp() - startversuch['starttime'].timestamp() - np.cumsum(svdf['RUN2'])['synchronize']
with warnings.catch_warnings():
warnings.simplefilter("ignore")
svdf = pd.concat([
svdf,
pd.DataFrame.from_dict(
{ 'maxload':['-',pl.val],
'ramp':['-',(pl.val / fsm._e['Power_PowerNominal']) * 100 / svdf.at['loadramp','RUN2']],
'cumstarttime':[np.cumsum(svdf['FSM'])['loadramp'], np.cumsum(svdf['RUN2'])['loadramp']]
},
columns=['FSM','RUN2'],
orient='index')]
)
ftitle = f"{fsm._e} ----- Start {startversuch['index']} {startversuch['mode']} | {'SUCCESS' if startversuch['success'] else 'FAILED'} | {startversuch['starttime'].round('S')}"
display(HTML('<h3>'+ f"{fsm._e} ----- Start {startversuch['index']} {startversuch['mode']} | {'SUCCESS' if startversuch['success'] else 'FAILED'} | {startversuch['starttime'].round('S')}" + '</h3>'))
fsm.disp_result(startversuch)
al_lines = fsm.disp_alarms(startversuch)
w_lines = fsm.disp_warnings(startversuch)
fig = dmyplant2.dbokeh_chart(data, dset, title=ftitle, figsize=p_figsize, style='line', line_width=0)
#fsm results
dmyplant2.add_dbokeh_vlines(fsm.states_lines(startversuch), fig,line_color='red', line_dash='solid', line_alpha=0.4)
#alarms & warnings
dmyplant2.add_dbokeh_vlines(al_lines,fig,line_color='purple', line_dash='dashed', line_alpha=1, line_width=2)
dmyplant2.add_dbokeh_vlines(w_lines,fig,line_color='brown', line_dash='dashed', line_alpha=1, line_width=2)
#fsm run 2 results
lcol='blue'
dmyplant2.add_dbokeh_vlines([sl.loc], fig,line_color=lcol, line_dash='solid', line_alpha=0.4)
dmyplant2.add_dbokeh_vlines([sr.loc], fig,line_color=lcol, line_dash='solid', line_alpha=0.4)
dmyplant2.add_dbokeh_vlines([pl.loc], fig,line_color=lcol, line_dash='solid', line_alpha=0.4)
dmyplant2.add_dbokeh_vlines([pr.loc], fig,line_color=lcol, line_dash='solid', line_alpha=0.4)
bokeh.plotting.show(fig)
print(f"Number of Starts in Result: {rdb.shape[0]}")
